OpenRaiser/Intern-Atlas huggingface dataset:
Eigen-Direction Alignment Based Physical-Layer Network Coding for MIMO Two-Way Relay Channels
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we propose a novel communication strategy which incorporates physical-layer network coding (PNC) into multiple-input multiple output (MIMO) two-way relay channels (TWRCs). At the heart of the proposed scheme lies a new key technique referred to as eigen-direction alignment (EDA) precoding. The EDA precoding efficiently aligns the two-user's eigen-modes into the same directions. Based on that, we carry out multi-stream PNC over the aligned eigen-modes. We derive an achievable rate of the proposed EDA-PNC scheme, based on nested lattice codes, over a MIMO TWRC. Asymptotic analysis shows that the proposed EDA-PNC scheme approaches the capacity upper bound as the number of user antennas increases towards infinity. For a finite number of user antennas, we formulate the design criterion of the optimal EDA precoder and present solutions. Numerical results show that there is only a marginal gap between the achievable rate of the proposed EDA-PNC scheme and the capacity upper bound of the MIMO TWRC, in the median-to-large SNR region. We also show that the proposed EDA-PNC scheme significantly outperforms existing amplify-and-forward and decode-and-forward based schemes for MIMO TWRCs.
On Geometric Upper Bounds for Positioning Algorithms in Wireless Sensor Networks
Venue: arXiv cs.IT 2012
Year: 2012

This paper studies the possibility of upper bounding the position error of an estimate for range based positioning algorithms in wireless sensor networks. In this study, we argue that in certain situations when the measured distances between sensor nodes are positively biased, e.g., in non-line-of-sight conditions, the target node is confined to a closed bounded convex set (a feasible set) which can be derived from the measurements. Then, we formulate two classes of geometric upper bounds with respect to the feasible set. If an estimate is available, either feasible or infeasible, the worst-case position error can be defined as the maximum distance between the estimate and any point in the feasible set (the first bound). Alternatively, if an estimate given by a positioning algorithm is always feasible, we propose to get the maximum length of the feasible set as the worst-case position error (the second bound). These bounds are formulated as nonconvex optimization problems. To progress, we relax the nonconvex problems and obtain convex problems, which can be efficiently solved. Simulation results indicate that the proposed bounds are reasonably tight in many situations.
DREAM: DiffeRentially privatE smArt Metering
Venue: arXiv cs.CR 2012
Year: 2012

This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees. With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics while learning only limited information about the activities of individual households. For example, a supplier cannot tell from a user's trace when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add noise to their data and encrypt the results with an efficient stream cipher.
An efficient FPGA implementation of MRI image filtering and tumor characterization using Xilinx system generator
Venue: arXiv cs.AR 2012
Year: 2012

This paper presents an efficient architecture for various image filtering algorithms and tumor characterization using Xilinx System Generator (XSG). This architecture offers an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explores important aspects concerned to hardware implementation. Performance of this architecture implemented in SPARTAN-3E Starter kit (XC3S500E-FG320) exceeds those of similar or greater resources architectures. The proposed architecture reduces the resources available on target device by 50%.
Hybrid GPS-GSM Localization of Automobile Tracking System
Venue: arXiv cs.SY 2012
Year: 2012

An integrated GPS-GSM system is proposed to track vehicles using Google Earth application. The remote module has a GPS mounted on the moving vehicle to identify its current position, and to be transferred by GSM with other parameters acquired by the automobile's data port as an SMS to a recipient station. The received GPS coordinates are filtered using a Kalman filter to enhance the accuracy of measured position. After data processing, Google Earth application is used to view the current location and status of each vehicle. This goal of this system is to manage fleet, police automobiles distribution and car theft cautions.
Impact Factor: outdated artefact or stepping-stone to journal certification?
Venue: arXiv cs.DL 2012
Year: 2012

A review of Garfield's journal impact factor and its specific implementation as the Thomson Reuters Impact Factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.
Automatic system for counting cells with elliptical shape
Venue: arXiv cs.CV 2012
Year: 2012

This paper presents a new method for automatic quantification of ellipse-like cells in images, an important and challenging problem that has been studied by the computer vision community. The proposed method can be described by two main steps. Initially, image segmentation based on the k-means algorithm is performed to separate different types of cells from the background. Then, a robust and efficient strategy is performed on the blob contour for touching cells splitting. Due to the contour processing, the method achieves excellent results of detection compared to manual detection performed by specialists.
Fast, parallel and secure cryptography algorithm using Lorenz's attractor
Venue: arXiv cs.CR 2012
Year: 2012

A novel cryptography method based on the Lorenz's attractor chaotic system is presented. The proposed algorithm is secure and fast, making it practical for general use. We introduce the chaotic operation mode, which provides an interaction among the password, message and a chaotic system. It ensures that the algorithm yields a secure codification, even if the nature of the chaotic system is known. The algorithm has been implemented in two versions: one sequential and slow and the other, parallel and fast. Our algorithm assures the integrity of the ciphertext (we know if it has been altered, which is not assured by traditional algorithms) and consequently its authenticity. Numerical experiments are presented, discussed and show the behavior of the method in terms of security and performance. The fast version of the algorithm has a performance comparable to AES, a popular cryptography program used commercially nowadays, but it is more secure, which makes it immediately suitable for general purpose cryptography applications. An internet page has been set up, which enables the readers to test the algorithm and also to try to break into the cipher in.
Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using Functional Data Analysis
Venue: arXiv cs.CV 2012
Year: 2012

This work proposes and study the concept of Functional Data Analysis transform, applying it to the performance improving of volumetric Bouligand-Minkowski fractal descriptors. The proposed transform consists essentially in changing the descriptors originally defined in the space of the calculus of fractal dimension into the space of coefficients used in the functional data representation of these descriptors. The transformed decriptors are used here in texture classification problems. The enhancement provided by the FDA transform is measured by comparing the transformed to the original descriptors in terms of the correctness rate in the classification of well known datasets.
Partial Degree Bounded Edge Packing Problem
Venue: arXiv cs.CC 2012
Year: 2012

In [1], whether a target binary string s can be represented from a boolean formula with operands chosen from a set of binary strings W was studied. In this paper, we first examine selecting a maximum subset X from W, so that for any string t in X, t is not representable by X\{t}. We rephrase this problem as graph, and surprisingly find it give rise to a broad model of edge packing problem, which itself falls into the model of forbidden subgraph problem. Specifically, given a graph G(V;E) and a constant c, the problem asks to choose as many as edges to form a subgraph G'. So that in G', for each edge, at least one of its endpoints has degree no more than c. We call such G' partial c degree bounded. When c = 1, it turns out to be the complement of dominating set. We present several results about hardness, approximation for the general graph and efficient exact algorithm on trees. This edge packing problem model also has a direct interpretation in resource allocation. There are n types of resources and m jobs. Each job needs two types of resources. A job can be accomplished if either one of its necessary resources is shared by no more than c other jobs. The problem then asks to nish as many jobs as possible. We believe this partial degree bounded graph problem merits more attention.
A Perfect Model for Bounded Verification
Venue: arXiv cs.FL 2012
Year: 2012

A class of languages C is perfect if it is closed under Boolean operations and the emptiness problem is decidable. Perfect language classes are the basis for the automata-theoretic approach to model checking: a system is correct if the language generated by the system is disjoint from the language of bad traces. Regular languages are perfect, but because the disjointness problem for CFLs is undecidable, no class containing the CFLs can be perfect. In practice, verification problems for language classes that are not perfect are often under-approximated by checking if the property holds for all behaviors of the system belonging to a fixed subset. A general way to specify a subset of behaviors is by using bounded languages (languages of the form w1* ... wk* for fixed words w1,...,wk). A class of languages C is perfect modulo bounded languages if it is closed under Boolean operations relative to every bounded language, and if the emptiness problem is decidable relative to every bounded language. We consider finding perfect classes of languages modulo bounded languages. We show that the class of languages accepted by multi-head pushdown automata are perfect modulo bounded languages, and characterize the complexities of decision problems. We also show that bounded languages form a maximal class for which perfection is obtained. We show that computations of several known models of systems, such as recursive multi-threaded programs, recursive counter machines, and communicating finite-state machines can be encoded as multi-head pushdown automata, giving uniform and optimal underapproximation algorithms modulo bounded languages.
Dynamic Spectrum Sharing Among Repeatedly Interacting Selfish Users With Imperfect Monitoring
Venue: arXiv cs.IT 2012
Year: 2012

We develop a novel design framework for dynamic distributed spectrum sharing among secondary users (SUs) who adjust their power levels to compete for spectrum opportunities while satisfying the interference temperature (IT) constraints imposed by primary users. The considered interaction among the SUs is characterized by the following three features. First, since the SUs are decentralized, they are selfish and aim to maximize their own long-term payoffs from utilizing the network rather than obeying the prescribed allocation of a centralized controller. Second, the SUs interact with each other repeatedly and they can coexist in the system for a long time. Third, the SUs have limited and imperfect monitoring ability: they only observe whether the IT constraints are violated, and their observation is imperfect due to the erroneous measurements. To capture these features, we model the interaction of the SUs as a repeated game with imperfect monitoring. We first characterize the set of Pareto optimal payoffs that can be achieved by deviation-proof spectrum sharing policies, which are policies that the selfish users find it in their interest to comply with. Next, for any given payoff in this set, we show how to construct a deviation-proof policy to achieve it. The constructed deviation-proof policy is amenable to distributed implementation, and allows users to transmit in a time-division multiple-access (TDMA) fashion. In the presence of strong multi-user interference, our policy outperforms existing spectrum sharing policies that dictate users to transmit at constant power levels simultaneously. Moreover, our policy can achieve Pareto optimality even when the SUs have limited and imperfect monitoring ability, as opposed to existing solutions based on repeated games, which require perfect monitoring abilities.
A Novel Methodology for Thermal Aware Silicon Area Estimation for 2D & 3D MPSoCs
Venue: arXiv cs.ET 2012
Year: 2012

In a multiprocessor system on chip (MPSoC) IC the processor is one of the highest heat dissipating devices. The temperature generated in an IC may vary with floor plan of the chip. This paper proposes an integration and thermal analysis methodology to extract the peak temperature and temperature distribution of 2-dimensional and 3-dimensional multiprocessor system-on-chip. As we know the peak temperature of chip increases in 3-dimensional structures compared to 2-dimensional ones due to the reduced space in intra-layer and inter-layer components. In sub-nanometre scale technologies, it is inevitable to analysis the heat developed in individual chip to extract the temperature distribution of the entire chip. With the technology scaling in new generation ICs more and more components are integrated to a smaller area. Along with the other parameters threshold voltage is also scaled down which results in exponential increase in leakage current. This has resulted in rise in hotspot temperature value due to increase in leakage power. In this paper, we have analysed the temperature developed in an IC with four identical processors at 2.4 GHz in different floorplans. The analysis has been done for both 2D and 3D arrangements. In the 3D arrangement, a three layered structure has been considered with two Silicon layers and a thermal interface material (TIM) in between them. Based on experimental results the paper proposes a methodology to reduce the peak temperature developed in 2D and 3D integrated circuits .
A New Color Feature Extraction Method Based on Dynamic Color Distribution Entropy of Neighborhoods
Venue: arXiv cs.CV 2012
Year: 2012

One of the important requirements in image retrieval, indexing, classification, clustering and etc. is extracting efficient features from images. The color feature is one of the most widely used visual features. Use of color histogram is the most common way for representing color feature. One of disadvantage of the color histogram is that it does not take the color spatial distribution into consideration. In this paper dynamic color distribution entropy of neighborhoods method based on color distribution entropy is presented, which effectively describes the spatial information of colors. The image retrieval results in compare to improved color distribution entropy show the acceptable efficiency of this approach.
Spatiotemporal Gabor filters: a new method for dynamic texture recognition
Venue: arXiv cs.CV 2012
Year: 2012

This paper presents a new method for dynamic texture recognition based on spatiotemporal Gabor filters. Dynamic textures have emerged as a new field of investigation that extends the concept of self-similarity of texture image to the spatiotemporal domain. To model a dynamic texture, we convolve the sequence of images to a bank of spatiotemporal Gabor filters. For each response, a feature vector is built by calculating the energy statistic. As far as the authors know, this paper is the first to report an effective method for dynamic texture recognition using spatiotemporal Gabor filters. We evaluate the proposed method on two challenging databases and the experimental results indicate that the proposed method is a robust approach for dynamic texture recognition.
Energy Efficiency Scaling Law for MIMO Broadcasting Channels
Venue: arXiv cs.IT 2012
Year: 2012

This letter investigates the energy efficiency (EE) scaling law for the broadcasting channels (BC) with many users, in which the non-ideal transmit independent power consumption is taken into account. We first consider the single antenna case with $K$ users, and derive that the EE scales as $\frac{\log_2 \ln K}α$ when $α> 0$ and $\log_2 K$ when $α= 0$, where $α$ is the normalized transmit independent power. After that, we extend it to the general MIMO BC case with a $M$-antenna transmitter and $K$ users each with $N$ antennas. The scaling law becomes $\frac{M \log_2 \ln NK}α$ when $α> 0$ and $ \log_2 NK$ when $α= 0$.
Performance Analysis of Observation Based Cooperation Enforcement in Ad Hoc Networks
Venue: arXiv cs.NI 2012
Year: 2012

Node misbehavior due to selfish or malicious behavior could significantly degrade the performance of MANET because most existing routing protocols in MANET aim to find the most efficient path. Overhearing and reputation based cooperation schemes have been used to detect and isolate the misbehaving nodes as well as to force them to cooperate. Performance analysis has been done for the network traffic using OCEAN over DSR on ns2 while considering the low energy levels for mobile nodes. Throughput, energy level, routing packets and normalized routing overhead are analyzed for OCEAN and normal DSR to show the impact of OCEAN on the overall network performance.
Network Analysis of Recurring YouTube Spam Campaigns
Venue: arXiv cs.SI 2012
Year: 2012

As the popularity of content sharing websites such as YouTube and Flickr has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to bogus e-commerce websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies. One of these strategies uses a small number of spam user accounts to comment on a large number of videos, whereas a larger number of accounts is used with the other. We present an evaluation that uses motif profiling to track two active campaigns matching these strategies, and identify some of the associated user accounts.
Image Labeling and Segmentation using Hierarchical Conditional Random Field Model
Venue: arXiv cs.CV 2012
Year: 2012

The use of hierarchical Conditional Random Field model deal with the problem of labeling images . At the time of labeling a new image, selection of the nearest cluster and using the related CRF model to label this image. When one give input image, one first use the CRF model to get initial pixel labels then finding the cluster with most similar images. Then at last relabeling the input image by the CRF model associated with this cluster. This paper presents a approach to label and segment specific image having correct information.
Managing Communication Latency-Hiding at Runtime for Parallel Programming Languages and Libraries
Venue: arXiv cs.DC 2012
Year: 2012

This work introduces a runtime model for managing communication with support for latency-hiding. The model enables non-computer science researchers to exploit communication latency-hiding techniques seamlessly. For compiled languages, it is often possible to create efficient schedules for communication, but this is not the case for interpreted languages. By maintaining data dependencies between scheduled operations, it is possible to aggressively initiate communication and lazily evaluate tasks to allow maximal time for the communication to finish before entering a wait state. We implement a heuristic of this model in DistNumPy, an auto-parallelizing version of numerical Python that allows sequential NumPy programs to run on distributed memory architectures. Furthermore, we present performance comparisons for eight benchmarks with and without automatic latency-hiding. The results shows that our model reduces the time spent on waiting for communication as much as 27 times, from a maximum of 54% to only 2% of the total execution time, in a stencil application.
A Complete Characterization of Irreducible Cyclic Orbit Codes and their Plücker Embedding
Venue: arXiv cs.IT 2012
Year: 2012

Constant dimension codes are subsets of the finite Grassmann variety. The study of these codes is a central topic in random linear network coding theory. Orbit codes represent a subclass of constant dimension codes. They are defined as orbits of a subgroup of the general linear group on the Grassmannian. This paper gives a complete characterization of orbit codes that are generated by an irreducible cyclic group, i.e. a group having one generator that has no non-trivial invariant subspace. We show how some of the basic properties of these codes, the cardinality and the minimum distance, can be derived using the isomorphism of the vector space and the extension field. Furthermore, we investigate the Plücker embedding of these codes and show how the orbit structure is preserved in the embedding.
On Detection-Directed Estimation Approach for Noisy Compressive Sensing
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we investigate a Bayesian sparse reconstruction algorithm called compressive sensing via Bayesian support detection (CS-BSD). This algorithm is quite robust against measurement noise and achieves the performance of a minimum mean square error (MMSE) estimator that has support knowledge beyond a certain SNR threshold. The key idea behind CS-BSD is that reconstruction takes a detection-directed estimation structure consisting of two parts: support detection and signal value estimation. Belief propagation (BP) and a Bayesian hypothesis test perform support detection, and an MMSE estimator finds the signal values belonging to the support set. CS-BSD converges faster than other BP-based algorithms, and it can be converted to a parallel architecture to become much faster. Numerical results are provided to verify the superiority of CS-BSD compared to recent algorithms.
Parameter Identification in a Probabilistic Setting
Venue: arXiv cs.NA 2012
Year: 2012

Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure. One group of methods is identified as updating the measure, the other group changes the measurable function. We connect both groups with the relatively recent methods of functional approximation of stochastic problems, and introduce especially in combination with the second group of methods a new procedure which does not need any sampling, hence works completely deterministically. It also seems to be the fastest and more reliable when compared with other methods. We show by example that it also works for highly nonlinear non-smooth problems with non-Gaussian measures.